I’m waiting for the tide to turn. My portfolio is growing very nicely this year – at least in USD terms. But I’ve recently got into options…
I still steadily hold a collection of longs (EBAY, AAPL, SNE) and shorts (EQR, MSFT), but I moved to replacing some of those shorts with “just out of the money longer term put options”. It is theoretically doing the same thing as shorting a stock, but it limits the downside risk to the value of the put I purchase. Let me explain…
A put option gives me the right to sell a certain number of shares at a certain value before a certain time.
Right now eBay shares are $36.77 each, and you can buy the right to sell those shares at $30 before Jan 2010 for a mere $3.90 a piece. You buy them in lots of 100, so that’s $390 to short 100 shares. That’s fantastic leverage – 100 shares is worth $3677, but you only have to outlay 1/10th of that.
That’s a good buy if you believe that between now and then there will be some sort of event that makes the shares drop towards $30. At $30 you could exercise your right to ‘put’ your shares to the market for $30. That doesn’t make sense, at exactly $30, as you’d be selling them for market price. However, if the price dropped to $25, then when you exercise the right to sell those shares to the maket for $30, and buy back the same number at $25, you’ll make a net $5 per share.
But wait – there is more, much more, and that is the time value of the option. The longer the time before the option expires, the larger the value of the option.
Between now and Jan 2010 there are a whole lot of events that could happen – markets and shares will go up and down. If the shares fall to $30, then that option will be much more valuable than just $5, as the probability that the shares will fall even further is higher. Thus the value of the option will be traded up in the market. Indeed there is as simple rule of thumb that traders use – always sell the option, never exercise early.
Right now the value of an option to put an eBay share at $40 is $7.50, which means you pay $4.50 odd over the current price of $37 for the time value of that option. Given eBay’s volatility over the last few years, that seems like a bargain. So is the market underpricing it?
Indeed -how does the market set the price?
The answer is that the price is set by traders, often at the command of hedge funds and the big banks. The trick is that these buyers and sellers are often using variants of a formula called Black Scholes (Nobel prize winning stuff) to value the options, so they come up with similar values. Others may be using Monte Carlo techniques (better) or their own proprietary models – built by ex-theoretical-physicists using mathematical tools and techniques of such particular complexity that they really don’t deserve to exist.
Black Scholes – the basic PDE ![]()
However, Black Scholes and many of the other models are fundamentally flawed, as they do not account for massive market events, or even massive events for a single stock. They use historical measures of volatility, and often do not plan for something called Heteroscedacity.
Go on – search for that word.
There are only 873 responses in Google, which indicates just how bad my spelling is how poorly the market understands the risks. Google Fat Tails, the layman’s term, then you’ll get a better response.
Actually, in that linked Wikipedia article is the phrase that pays:
The Black-Scholes model of option pricing is based on a normal distribution and under-prices options that are far out of the money since a 5 or 7 sigma event is more likely than the normal distribution predicts.
And that also means that you can lose your shirt if you think the market is based on a lovely bell-curve like Normal distribution. Events happen, stock prices rise and plummet sharply, and shirts are lost and won.
One of those events was the recent fall in the Kiwi dollar. You can bet that there were more than a few upset Japanese housewives that had failed to exit when the going was good, or worse yet bought leveraged Kiwi dollars near the peak.
Another event that the market isn’t pricing is a possible sharp fall in housing prices. What we saw recently was a ripple from the first USA version of that shock. There could be more to come. Much more.
So -here’s my non-diversified self managed portfolio – 43.8% up YTD. How long will it last I wonder?


And that also means that you can lose your shirt if you think the market is based on a lovely bell-curve like Normal distribution
No, there has been empirical evidence from research over recent years, that the market price follows a power-law probability distribution rather than the log-normal probability distribution. The Black-Scholes option pricing model was solved by the inventors (Economics Nobel laureates) using the Heat Equation from thermo-dynamics (statistical mechanics/physics). Again, Black-Scholes was a good start to revolutionize the development of pricing options. There are tons of algorithms, that has surpassed the the Black-Scholes, such as Tri-nomial tree, Binomial tree, Jump Diffusion, Implicit Difference, Roll-Geske-Whaley, Barone-Adesi-Whaley, etc,… I have written more than 25 algorithms for pricing of options (European, American, Exotic & non-standard) to be used in a web-based financial analytic software I am currently developing.
Econo-physicists have published a lot of stuff over recent years using Physics principles in an attempt to solve some difficulties that arise in Economics. The work of Econo-physicists re published in Physics journals and not economic journals, such as Physica journal and a few others. The Black-Scholes was based on brownian motion assumption of the price, however over recent years, Econo-physicists, have modeled price movement using path-integral of Quantum Mechanics, which developed by Physics Nobel Laureate Richard Feynman. Here are some interesting magazine articles.
#1) Quantum physics meets classical finance
#2) Is Economics the Next Physical Science?
#3) Power Laws are Connecting Boltzmann energy distribution to the Pareto wealth distribution
The contribution of physics to economics is still in its infancy, and I wouldn’t be surprised if it becomes formalize in the future, so as there would be papers taught about econo-physics for both finance and physics students.
I thought that I would never had to look in to my Quantum Mechanics old text books that has been sitting in my bookshelf for ages, but since I started developing economic/finance models, I have dusted off some of my books to go thru some of the models in order for me to able to develop econo-physics algorithm for my web-based application.
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Google Fat Tails, the layman’s term
Here is what an econo-physicist has to say about how market follows a power-law probability distribution, rather than a log-normal probability distribution.
A unified econophysics explanation for the power law exponents of stock market activity
There is a whole lot of published econo-physics stuff here.
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I agree – it sure as anything doesn’t follow a normal distribution… a power law distribution makes much more sense. So we both agree that a normal distribution will get you into a bad state.
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Others may be using Monte Carlo techniques (better) or their own proprietary models – built by ex-theoretical-physicists using mathematical tools and techniques of such particular complexity that they really don’t deserve to exist.
My monte-carlo option model predicts better than my others, however there is no model to date that does it 100%. The problem with monte-carlo option model is that it takes longer to compute and requires a huge computer memory for computation. The monte-carlo option models (2 of them) that I have written takes about 8 minutes to run on my PC for a simulation-iteration of 20 millions (20,000,000) for just a single asset. Increasing the number of iteration run to more than this 20 millions leads to my PC being crashed (out of memory). I set the default number of iteration to 1 million , which takes about 12 seconds to compute. The higher the number of iteration run, the better the price prediction .Some large financial institutions do use super-computer for market stress testing by running large-scale monte-carlo multi-asset simulations.
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